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Transform operational productivity and create new capabilities at scale with AI

Faculty helps companies realise enduring strategic advantage through better management of assets, infrastructure, networks and processes.

Through our Intelligent Asset Performance solutions, we help organisations to:

  • Increase operational productivity
  • Reduce costs
  • Add new capabilities

We work with customers to maximise outcomes for their business, society and the environment.

Our solutions

Automated inspection

Monitor the location, condition, performance and safety of your assets and infrastructure.

Failure prediction and prevention

Forecast faults, avoid downtime and extend asset and infrastructure lifetimes.

Expert assessment

Understand and optimise the performance of your assets and infrastructure.

Complex system optimisation

Improve the performance of complex networks or systems of assets and people.


We support in three key areas

Strategy

We help leadership teams design and execute the right AI adoption strategy for their organisation, identifying the opportunities that will deliver maximum return on investment.

Software

We build and deploy custom machine learning software to help our customers rapidly solve their most important problems.

Skills

We help teams to grow their in-house data science capability, so they can independently build and maintain AI models.

Our customers

Our team

Dr Arès Méroueh

Lead Data Scientist, Engineering and the Built Environment

Ares has overseen the development of several projects in the Engineering and Built Environment field, including work on reinforcement learning algorithms to generate designs and end-to-end pipelines that accurately detect, geolocate and measure engineering assets and their surroundings from video footage.

Prior to joining Faculty, Ares completed a PhD thesis in Pure Mathematics from the University of Cambridge, where he also did his undergraduate studies in Mathematics. His doctoral research focused on the mathematics of discrete structures, such as networks, and has been published in leading journals in the field.

Caroline Ames

Engagement Manager, Engineering and the Built Environment

Caroline has three years of experience managing the delivery of applied machine learning projects that drive innovation and efficiency across the Engineering and Built Environment sector. These range from using computer vision techniques with 3D point cloud and video data to perform asset inspection activities, to using reinforcement learning to perform generative design tasks.

Caroline has a Masters in Engineering from the University of Oxford, where she also completed graduate research modelling automotive powertrains as part of the Energy and Power Group.

Dr David Sauerwein

Data Scientist, Engineering and the Built Environment

Whilst at Faculty, David has worked on a variety of projects, from deploying tools such as computer vision libraries in the Built Environment, to the removal of terrorist content from social media platforms using deep learning techniques.

David has a PHD in Quantum Physics from the Leopold-Franzens Universität Innsbruck and was a postdoctoral researcher at the Max Planck Institute of Quantum Optics, where he developed new methods of information processing beyond the capabilities of conventional computers. His work also characterised correlations in so-called tensor network states, which are used in fields ranging from condensed matter theory to string theory and machine learning.

Dr Vickie Li

Data Scientist, Engineering and the Built Environment

In her time at Faculty, Vickie has worked on a diverse range of problems, ranging from building dashboards to improve the efficiency of healthcare appointment scheduling, to price forecasting on an agriculture commodity for a private equity investor.

Before joining Faculty, Vickie was awarded a DPhil from the University of Oxford. She developed a unifying computational framework to understand suboptimal human decisions. She has extensive experience in experimental design, online data collection and data analysis with machine learning techniques and statistical inference.

To find out more about what Faculty can do for you and your organisation, get in touch.

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